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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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NASA NTRS1987

Statistical aspects of solar flares

A survey of the statistical properties of 850 H alpha solar flares during 1975 is presented. Comparison of the results found here with those reported elsewhere for different epochs is accomplished. Distributions of rise time, decay time, and duration are given, as are the mean, mode, median, and 90th percentile values. Proportions by selected groupings are also determined. For flares in general, mean values for rise time, decay time, and duration are 5.2 + or - 0.4 min, and 18.1 + or 1.1 min, respectively. Subflares, accounting for nearly 90 percent of the flares, had mean values lower than those found for flares of H alpha importance greater than 1, and the differences are statistically significant. Likewise, flares of bright and normal relative brightness have mean values of decay time and duration that are significantly longer than those computed for faint flares, and mass-motion related flares are significantly longer than non-mass-motion related flares. Seventy-three percent of the mass-motion related flares are categorized as being a two-ribbon flare and/or being accompanied by a high-speed dark filament. Slow rise time flares (rise time greater than 5 min) have a mean value for duration that is significantly longer than that computed for fast rise time flares, and long-lived duration flares (duration greater than 18 min) have a mean value for rise time that is significantly longer than that computed for short-lived duration flares, suggesting a positive linear relationship between rise time and duration for flares. Monthly occurrence rates for flares in general and by group are found to be linearly related in a positive sense to monthly sunspot number. Statistical testing reveals the association between sunspot number and numbers of flares to be significant at the 95 percent level of confidence, and the t statistic for slope is significant at greater than 99 percent level of confidence. Dependent upon the specific fit, between 58 percent and 94 percent of the variation can be accounted for with the linear fits. A statistically significant Northern Hemisphere flare excess (P less than 1 percent) was found, as was a Western Hemisphere excess (P approx 3 percent). Subflares were more prolific within 45 deg of central meridian (P less than 1 percent), while flares of H alpha importance or = 1 were more prolific near the limbs greater than 45 deg from central meridian; P approx 2 percent). Two-ribbon flares were more frequent within 45 deg of central meridian (P less than 1 percent). Slow rise time flares occurred more frequently in the western hemisphere (P approx 2 percent), as did short-lived duration flares (P approx 9 percent), but fast rise time flares were not preferentially distributed (in terms of east-west or limb-disk). Long-lived duration flares occurred more often within 45 deg 0 central meridian (P approx 7 percent). Mean durations for subflares and flares of H alpha importance or + 1, found within 45 deg of central meridian, are 14 percent and 70 percent, respectively, longer than those found for flares closer to the limb. As compared to flares occurring near cycle maximum, the flares of 1975 (near solar minimum) have mean values of rise time, decay time, and duration that are significantly shorter. A flare near solar maximum, on average, is about 1.6 times longer than one occurring near solar minimum.

Wilson, Robert M.↗
NASA NTRS1990

Reliability growth modeling analysis of the space shuttle main engines based upon the Weibull process

The Weibull process, identified as the inhomogeneous Poisson process with the Weibull intensity function, is used to model the reliability growth assessment of the space shuttle main engine test and flight failure data. Additional tables of percentage-point probabilities for several different values of the confidence coefficient have been generated for setting (1-alpha)100-percent two sided confidence interval estimates on the mean time between failures. The tabled data pertain to two cases: (1) time-terminated testing, and (2) failure-terminated testing. The critical values of the three test statistics, namely Cramer-von Mises, Kolmogorov-Smirnov, and chi-square, were calculated and tabled for use in the goodness of fit tests for the engine reliability data. Numerical results are presented for five different groupings of the engine data that reflect the actual response to the failures.

Wheeler, J. T.↗
NASA NTRSDate not supplied

DO 365 Test Procedure Results and Analysis Discussion

CAL will leverage M&S tools and existing encounter data to evaluate the robustness of the DAA MOPS Test Procedures. Recently, the SC-147 ACAS-Xu team performed an analysis evaluating the Xu implementation against the DAA MOPS test procedures. Several areas of non-compliance were raised, some indicating shortfalls in our DAA test procedures and/or requirements. The ACAS-Xu team is expected to put out a report highlighting these exemptions. This work will build off that ACAS-Xu analysis/report and perform a full characterization of the statistical test procedures and document the findings. Additionally, CAL will provide recommendations for addressing these findings, potentially resulting in changes to test procedures and/or requirements.

ACAS-Xu↗
NASA NTRS1990

Thermal advection and stratification effects on surface winds and the low level meridional mass transport

Statistical tests are performed on the Seasat scatterometer observations to examine if and to what degree thermal advection and stratification effects manifest themselves in these remotely sensed measurements of mean wind and wind stress over the ocean. On the basis of a two layer baroclinic boundary layer model which is presented, it is shown that the thermal advection and stratification of the entire boundary layer as well as the geostrophic forcing influence the modeled near surface wind and wind stress profiles. Evidence of diurnal variation in the stratification under barotropic conditions is found in the data, with the daytime marine boundary layer being more convective than its nighttime counterpart. The temporal and spacial sampling pattern of the satellite makes it impossible to recover the full diurnal cycle, however. The observed effects of the thermal advection are shown to be statistically significant during the day (and presumed more convective) hours, causing a systematic increase in the poleward transport of mass and heat. The statistical results are in a qualitative agreement with the model simulations and cannot be reproduced in randomized control tests.

Levy, Gad↗
NASA NTRS1979

Application of modified profile analysis to function testing of the motion/no-motion issue in an aircraft ground-handling simulation

A recent modification of the methodology of profile analysis, which allows the testing for differences between two functions as a whole with a single test, rather than point by point with multiple tests is discussed. The modification is applied to the examination of the issue of motion/no motion conditions as shown by the lateral deviation curve as a function of engine cut speed of a piloted 737-100 simulator. The results of this application are presented along with those of more conventional statistical test procedures on the same simulator data.

Parrish, R. V.↗
NASA NTRS1983

Slow crack growth in spinel in water

Magnesium aluminate spinel was tested in a water environment at room temperature to establish its slow crack-growth behavior. Ring specimens with artificial flaws on the outside surface were loaded hydraulically on the inside surface. The time to failure was measured. Various precracking techniques were evaluated and multiple precracks were used to minimize the scatter in the static fatigue tests. Statistical analysis techniques were developed to determine the strength and crack velocities for a single flaw. Slow crack-growth rupture was observed at stress intensities as low as 70 percent of K sub c. A strengthening effect was observed in specimens that had survived long-time static fatigue tests.

Schwantes, S.↗
NASA NTRS2013

Closed-Loop Analysis of Soft Decisions for Serial Links

We describe the benefit of using closed-loop measurements for a radio receiver paired with a counterpart transmitter. We show that real-time analysis of the soft decision output of a receiver can provide rich and relevant insight far beyond the traditional hard-decision bit error rate (BER) test statistic. We describe a Soft Decision Analyzer (SDA) implementation for closed-loop measurements on single- or dual- (orthogonal) channel serial data communication links. The analyzer has been used to identify, quantify, and prioritize contributors to implementation loss in live-time during the development of software defined radios. This test technique gains importance as modern receivers are providing soft decision symbol synchronization as radio links are challenged to push more data and more protocol overhead through noisier channels, and software-defined radios (SDRs) use error-correction codes that approach Shannon's theoretical limit of performance.

Lansdowne, Chatwin A.↗
NASA NTRS2013

Soft Decision Analyzer

We describe the benefit of using closed-loop measurements for a radio receiver paired with a counterpart transmitter. We show that real-time analysis of the soft decision output of a receiver can provide rich and relevant insight far beyond the traditional hard-decision bit error rate (BER) test statistic. We describe a Soft Decision Analyzer (SDA) implementation for closed-loop measurements on single- or dual- (orthogonal) channel serial data communication links. The analyzer has been used to identify, quantify, and prioritize contributors to implementation loss in live-time during the development of software defined radios. This test technique gains importance as modern receivers are providing soft decision symbol synchronization as radio links are challenged to push more data and more protocol overhead through noisier channels, and software-defined radios (SDRs) use error-correction codes that approach Shannon's theoretical limit of performance.

Lansdowne, Chatwin↗
NASA NTRS2005

Influence of Coronal Abundance Variations

The PI of this project was Jeff Scargle of NASA/Ames. Co-I's were Alma Connors of Eureka Scientific/Wellesley, and myself. Part of the work was subcontracted to Eureka Scientific via SAO, with Vinay Kashyap as PI. This project was originally assigned grant number NCC2-1206, and was later changed to NCC2-1350 for administrative reasons. The goal of the project was to obtain, derive, and develop statistical and data analysis tools that would be of use in the analyses of high-resolution, high-sensitivity data that are becoming available with new instruments. This is envisioned as a cross-disciplinary effort with a number of "collaborators" including some at SA0 (Aneta Siemiginowska, Peter Freeman) and at the Harvard Statistics department (David van Dyk, Rostislav Protassov, Xiao-li Meng, Epaminondas Sourlas, et al). We have developed a new tool to reliably measure the metallicities of thermal plasma. It is unfeasible to obtain high-resolution grating spectra for most stars, and one must make the best possible determination based on lower-resolution, CCD-type spectra. It has been noticed that most analyses of such spectra have resulted in measured metallicities that were significantly lower than when compared with analyses of high- resolution grating data where available (see, e.g., Brickhouse et al., 2000, ApJ 530,387). Such results have led to the proposal of the existence of so-called Metal Abundance Deficient, or "MAD" stars (e.g., Drake, J.J., 1996, Cool Stars 9, ASP Conf.Ser. 109, 203). We however find that much of these analyses may be systematically underestimating the metallicities, and using a newly developed method to correctly treat the low-counts regime at the high-energy tail of the stellar spectra (van Dyk et al. 2001, ApJ 548,224), have found that the metallicities of these stars are generally comparable to their photospheric values. The results were reported at the AAS (Sourlas, Yu, van Dyk, Kashyap, and Drake, 2000, BAAS 196, v32, #54.02), and at the conference on Statistical Challenges in Modem Astronomy (Sourlas, van Dyk, Kashyap, Drake, and Pease, 2003, SCMA 111, Eds. E.D.Feigelson, G.J.Babu, New York:Springer, p489-490). We also described the limitations of one of the most egregiously misused and misapplied statistical tests in astrophysical literature, the F-test for verifying model components (Protassov, van Dyk, Connors, Kashyap, and Siemiginowska, 2002, ApJ, 571,545). Indeed, a search through the ApJ archives turned up 170 papers in the 5 previous years that used the F-test explicitly in some form or the other, and with the vast majority of them not using it correctly! Indeed, looking at just 4 issues of the ApJ in 2001, we found 13 instances of its use, of which nine were demonstrably incorrect. Clearly, it is difficult to understate the importance of this issue. We also worked on speeding up Bayes Blocks and Sparse Bayes Blocks algorithms to make them more tractable for large searches. We also supported staistics students and postdocs in both explicit physics- model-based (spectra with tens of thousands of atomic lines) and "model-free" -- i.e. non-parametric or semi-parametric -- algorithms. Work on using more of the latter is just beginning; while using multi-scale methods for Poisson imaging has come to hition. In fact, "An Image Restoration Technique with Error Estimates", by D. Esch, A. Connors, M. Karovska, and D. van Dyk, was published by ApJ (Esch et a1.2004, ApJ, 610, 1213). The code has been delivered to M. Karovska for CXC; and is available for beta-testing upon request. The other large project we worked on was on the self-consistent modeling of logN-logs curves in the Poisson limit. logN-logs curves are a fundamental tool in the study of source populations, luminosity functions, and cosmological parameters. However, their determination is hampered by statistical effects such as the Eddington bias, incompleteness due to detection efficiency, faint source flux fluctuations, etc. We have develed a new and powerful method using the full Poisson machinery that allows us to model the logN-logs distribution of X-ray sources in a self-consistent manner. Because we properly account for all the above statistical effects, our modeling is valid over the full range of the data, and not just for strong sources, as is normally done. Using a Bayesian approach and modeling the fluxes with known functional forms such as simple or broken power-laws, and conditioning the expected photon counts on the fluxes, the background contamination, effective area, detector vignetting, and detection probability, we can delve deeply into the low counts regime and extend the usefulness of medium sensitivity surveys such as ChAMP by orders of magnitude. The built-in flexibility of the algorithm also allows a simultaneous analysis of multiple datasets. We have applied this analysis to a set a Chandra observations (Sourlas, Kashyap, Zezas, van Dyk, 2004, HEAD #8, #16.32)

Scargle, Jeffrey D.↗
NASA NTRS1996

The Effects of Using Space to Teach Standard Elementary School Curriculum

This brief report and recommendation for further research brings to a formal close this effort, the original purpose of which is described in detail in The effects of using space to teach standard elementary school curriculum, Volume 1, included here as the Appendix. Volume 1 describes the project as a 3-year research program to determine the effectiveness of using space to teach. The research design is quasi experimental using standardized test data on students from Aldrin Elementary School and a District-identified 'control' school, which shall be referred to as 'School B.' Students now in fourth through sixth grades will be compared now (after one year at Aldrin) and tracked at least until the present sixth graders are through the eighth grade. Appropriate statistical tests will be applied to standardized test scores to see if Aldrin students are 'better' than School B students in areas such as: Overall academic performance; Performance in math/science; and Enrollments in math/science in middle school.

Ewell, Robert N.↗
NASA NTRS1986

The effects of expressivity and flight task on cockpit communication and resource management

The results of an investigation to develop a methodology for evaluating crew communication behavior on the flight deck and a flight simulator experiment to test the effects of crew member expressivity, as measured by the Personal Attributes Questionnarie, and flight task on crew communication and flight performance are discussed. A methodology for coding and assessing flight crew communication behavior as well as a model for predicting that behavior is advanced. Although not enough crews were found to provide valid statistical tests, the results of the study tend to indicate that crews in which the captain has high expressivity perform better than those whose captain is low in expressivity. There appears to be a strong interaction between captains and first officers along the level of command dimension of communication. The PAQ appears to identify those pilots who offer disagreements and inititate new subjects for discussion.

Jensen, R. S.↗
NASA NTRS2004

A Meteorological Model's Dependence on Radiation Update Frequency

Numerical weather models are used to simulate circulations in the atmosphere including clouds and precipitation by applying a set of mathematical equations over a three-dimensional grid. The grid is composed of discrete points at which the meteorological variables are defined. As computing power continues to rise these models are being used at finer grid spacing, but they must still cover a wide range of scales. Some of the physics that must be accounted for in the model cannot be explicitly resolved, and their effects, therefore, must be estimated or "parameterized". Some of these parameterizations are computationally expensive. To alleviate the problem, they are not always updated at the time resolution of the model with the assumption being that the impact will be small. In this study, a coupled land-atmosphere model is used to assess the impact of less frequent updates of the computationally expensive radiation physics for a case on June 6, 2002, that occurred during a field experiment over the central plains known as International H20 Project (IHOP). The model was tested using both the original conditions, which were dry, and with modified conditions wherein moisture was added to the lower part of the atmosphere to produce clouds and precipitation (i.e., a wet case). For each of the conditions (i.e., dry and wet), four set of experiments were conducted wherein the model was run for a period of 24 hours and the radiation fields (including both incoming solar and outgoing longwave) were updated every 1, 3, 10, and 100 time steps. Statistical tests indicated that average quantities of surface variables for both the dry and wet cases were the same for the various update frequencies. However, spatially the results could be quite different especially in the wet case after it began to rain. The near-surface wind field was found to be different most of the time even for the dry case. In the wet case, rain intensities and average vertical profiles of heating associated with cloudy areas were found to differ for the various radiation update frequencies. The latter implies that the mean state of the model could be different as a result of not updating the radiation fields every time step and has important implications for longer term climate studies

Eastman, Joseph L.↗
NASA NTRS1977

Testing the accuracy of remote sensing land use maps

Some of the main aspects that need to be considered in a remote sensing sampling design are: (1) the frequency that any one land use type (on the ground) is erroneously attributed to another class by the interpreter; (2) the frequency that the wrong land use (as observed on the ground) is erroneously included in any one class by the remote sensing interpreter; (3) the proportion of all land (as determined in the field) that is mistakenly attributed by the interpreter; and (4) the determination of whether the mistakes are random (so that the overall proportions are approximately correct) or subject to a persistent bias. A sampling and statistical testing procedure is presented which allows an approximate answer to each of these aspects. The concept developed and described incorporates the probability of making incorrect interpretations at particular prescribed accuracy levels, for a certain number of errors, for a particular sample size. It is considered that this approach offers a meaningful explanation of the interpretation accuracy level of an entire remote sensing land use survey.

Vangenderen, J. L.↗
NASA NTRS1984

The rotation of small asteroids

The Binzel and Mulholland (1983) sample of photoelectrically determined rotational parameters for 17 main belt asteroids with diameters less than 30 km are compared with previous observations of asteroids of that size range. Rigorous statistical tests are applied to investigate bias effects and quantify results on asteroid rotation. The samples are described and compared for rotational frequency, rotational amplitude, frequency distribution, and diameter and frequency dependence. It is concluded that the observed rotational frequency distribution can be acceptably fit by two Maxwellian distributions, which is consistent with the hypothesis that there are separate populations of slow and fast rotating asteroids. The frequency distributions of main belt asteroids less than 15 km in diameter and earth and Mars crossers do not differ significantly, but the larger mean lightcurve amplitude of these crossers is statistically significant. No significant diameter dependence on rotational frequency is found among the sample asteroids.

Binzel, R. P.↗
NASA NTRS1997

Impact Crises, Mass Extinctions, and Galactic Dynamics: A Unified Theory

A general hypothesis linking mass extinctions of life with impacts of large asteroids and comets is based on astronomical data, impact dynamics, and geological information. The waiting times of large-body impacts on the Earth, derived from the flux of Earth-crossing asteroids and comets, and the estimated size of impacts capable of causing large-scale environmental disasters predict that impacts of objects (sup 3)5 km in diameter ((sup 3)10(exp 7) Mt TNT equivalent) could be sufficient to explain the record of about 25 extinction pulses in the last 540 m.y., with the five recorded major mass extinctions related to the impacts of the largest objects of (sup 3)10 km in diameter ( (sup 3)10(exp 8) Mt events). Smaller impacts (about 10(exp 6)-10(exp 7) Mt), with significant regional and even global environmental effects, could be responsible for the lesser boundaries in the geologic record. Tests of the "kill curve" relationship for impact-induced extinctions based on new data on extinction intensities and several well-dated large impact craters suggest that major mass extinctions require large impacts, and that a step in the kill curve may exist at impacts that produce craters of -100 km diameter, with smaller impacts capable of only relatively weak extinction pulses. Single impact craters < about 60 km in diameter should not be associated with global extinction pulses detectable in the Sepkoski database (although they may explain stage and zone boundaries marked by lesser faunal turnover), but multiple impacts in that size range may produce significant stepped extinction pulses. Statistical tests of the last occurrences of species at mass-extinction boundaries are generally consistent with predictions for abrupt or stepped extinctions, and several boundaries are known to show "catastrophic" signatures of environmental disasters and biomass crash, impoverished postextinction fauna and flora dominated by stress-tolerant and opportunistic species, and gradual ecological recovery and radiation of new taxa. Isotopic and other geochemical signatures are also generally consistent with the expected after-effects of catastrophic impacts. Seven of the recognized extinction pulses are associated with concurrent (in some cases multiple) stratigraphic impact markers (e.g., layers with high Ir, shocked minerals, microtektites), and/or large, dated impact craters. Other less-well-studied crisis intervals show elevated Ir, still well below that of the K/T spike, which might be explained by low-Ir impactors, ejecta blowoff, or the sedimentary reworking and dilution of impact signatures. The best explanation for a possible periodic component of about 30 m.y. in mass extinctions and clusters of impacts is the modulation of the comet flux associated with the solar system's periodic passage through the plane of the Milky Way Galaxy. The quantitative agreement among paleontological, geological, and astronomical data suggests an important underlying unification of the processes involved.

Rampino, M.R.↗
NASA NTRSDate not supplied

Using Machine Learning to Infer Material Properties of Debris Fragments from X-ray Images in the DebriSat Project

The DebriSat project is a collaboration effort with the NASA Orbital Debris Program Office, the U.S. Space Force Space Systems Command Center, The Aerospace Corporation, and the University of Florida. To date, over 200,000 fragments from this ground-based, hypervelocity impact experiment have been collected, and processing is underway to determine their physical characteristics, such as material, shape, color, characteristic length, and average cross-sectional area. The x-ray process is primarily used to identify the location of the fragments and estimated size for extraction, so that these physical characteristics can be assessed. This paper proposes a machine learning-based approach to characterize materials from x-ray images of debris fragments embedded in soft-catch foam used in the DebriSat project. The novel methodology discussed in this paper will highlight the use of x-ray imagery data to characterize these fragments without extraction or a human-in-the-loop. Both supervised and unsupervised machine learning techniques are utilized with this approach to infer the physical parameters of the fragments embedded in the soft-catch foam panels used in the impact experiment based on x-ray images of the foam panels. Additionally, 3D reconstructions of the extracted fragments are created with images taken from two different angles using the structure from motion (SfM) method. The characteristic lengths and shape from the 3D reconstruction, alongside the physical characteristics of the debris, are used in the inference of the material type. To develop and test the approach, a dataset of x-ray images of debris fragments of varying sizes and materials is collected. Supervised learning methods such as convolutional neural networks (CNNs), support vector machines (SVM), decision trees, and random forest classifiers are used due to the high-dimensional feature spaces of the debris and nonlinear decision boundaries for material categorization. Given the limited pre-labeled data of embedded debris materials smaller than 10 mm, unsupervised machine learning techniques such as clustering algorithms and autoencoders are used, in addition to supervised learning methods. The clustering algorithms group similar fragments together based on their physical properties, and autoencoders reduce the dimensionality of the x ray images and extract relevant features. The performance of the proposed approach's is analyzed using a range of statistical methods, including confusion matrices, receiver operating characteristic curves, and precision-recall curves. The results are compared with those obtained using a baseline approach that relies on manual identification and classification of debris fragments. To evaluate the effectiveness of different machine learning methods, statistical tests such as t-tests, ANOVA, and cross-validation are performed, comparing the performance of CNNs, SVMs, clustering algorithms, and autoencoders. Additional analysis needs to be conducted to identify any sources of bias or variability that may affect the results, such as variations in imaging conditions or fragmentation patterns. Other topics explored are limitations, refinements, and the potential use of semi-supervised learning techniques, such as self-training to label unlabeled datasets and co-training using x-ray images taken from two different angles as two different models.

Saik Anam Siam↗
NASA NTRS2023

Using Machine Learning to Infer Material Properties of Debris Fragments from X-ray Images in the DebriSat Project

The DebriSat project is a collaboration effort with the NASA Orbital Debris Program Office, the U.S. Space Force Space Systems Command Center, The Aerospace Corporation, and the University of Florida. To date, over 200,000 fragments from this ground-based, hypervelocity impact experiment have been collected, and processing is underway to determine their physical characteristics, such as material, shape, color, characteristic length, and average cross-sectional area. The x-ray process is primarily used to identify the location of the fragments and estimated size for extraction, so that these physical characteristics can be assessed. This paper proposes a machine learning-based approach to characterize materials from x-ray images of debris fragments embedded in soft-catch foam used in the DebriSat project. The novel methodology discussed in this paper will highlight the use of x-ray imagery data to characterize these fragments without extraction or a human-in-the-loop. Both supervised and unsupervised machine learning techniques are utilized with this approach to infer the physical parameters of the fragments embedded in the soft-catch foam panels used in the impact experiment based on x-ray images of the foam panels. Additionally, 3D reconstructions of the extracted fragments are created with images taken from two different angles using the structure from motion (SfM) method. The characteristic lengths and shape from the 3D reconstruction, alongside the physical characteristics of the debris, are used in the inference of the material type. To develop and test the approach, a dataset of x-ray images of debris fragments of varying sizes and materials is collected. Supervised learning methods such as convolutional neural networks (CNNs), support vector machines (SVM), decision trees, and random forest classifiers are used due to the high-dimensional feature spaces of the debris and nonlinear decision boundaries for material categorization. Given the limited pre-labeled data of embedded debris materials smaller than 10 mm, unsupervised machine learning techniques such as clustering algorithms and autoencoders are used, in addition to supervised learning methods. The clustering algorithms group similar fragments together based on their physical properties, and autoencoders reduce the dimensionality of the x ray images and extract relevant features. The performance of the proposed approach's is analyzed using a range of statistical methods, including confusion matrices, receiver operating characteristic curves, and precision-recall curves. The results are compared with those obtained using a baseline approach that relies on manual identification and classification of debris fragments. To evaluate the effectiveness of different machine learning methods, statistical tests such as t-tests, ANOVA, and cross-validation are performed, comparing the performance of CNNs, SVMs, clustering algorithms, and autoencoders. Additional analysis needs to be conducted to identify any sources of bias or variability that may affect the results, such as variations in imaging conditions or fragmentation patterns. Other topics explored are limitations, refinements, and the potential use of semi-supervised learning techniques, such as self-training to label unlabeled datasets and co-training using x-ray images taken from two different angles as two different models.

Saik Anam Siam↗